IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i1p127-d125898.html
   My bibliography  Save this article

Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation

Author

Listed:
  • Oseweuba Valentine Okoro

    (Department of Physics, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

  • Zhifa Sun

    (Department of Physics, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

  • John Birch

    (Department of Food Science, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand)

Abstract

In response to existing global focus on improved biodiesel production methods via highly efficient catalyst-free high temperature and high pressure technologies, this study considered the comparative study of catalyst-free technologies for biodiesel production as an important research area. In this study, therefore, catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification and catalyst-free one step supercritical transesterification processes for biodiesel production have been evaluated via undertaking straight forward comparative energetic and environmental assessments. Energetic comparisons were undertaken after heat integration was performed since energy reduction has favourable effects on the environmental performance of chemical processes. The study confirmed that both processes are capable of producing biodiesel of high purity with catalyst-free integrated subcritical lipid hydrolysis and supercritical esterification characterised by a greater energy cost than catalyst-free one step supercritical transesterification processes for an equivalent biodiesel productivity potential. It was demonstrated that a one-step supercritical transesterification for biodiesel production presents an energetically more favourable catalyst-free biodiesel production pathway compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. The one-step supercritical transesterification for biodiesel production was also shown to present an improved environmental performance compared to the integrated subcritical lipid hydrolysis and supercritical esterification biodiesel production process. This is because of the higher potential environment impact calculated for the integrated subcritical lipid hydrolysis and supercritical esterification compared to the potential environment impact calculated for the supercritical transesterification process, when all material and energy flows are considered. Finally the major contributors to the environmental outcomes of both processes were also clearly elucidated.

Suggested Citation

  • Oseweuba Valentine Okoro & Zhifa Sun & John Birch, 2018. "Catalyst-Free Biodiesel Production Methods: A Comparative Technical and Environmental Evaluation," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:127-:d:125898
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/1/127/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/1/127/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    2. Rincón, L.E. & Jaramillo, J.J. & Cardona, C.A., 2014. "Comparison of feedstocks and technologies for biodiesel production: An environmental and techno-economic evaluation," Renewable Energy, Elsevier, vol. 69(C), pages 479-487.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moreno-Sader, K. & Meramo-Hurtado, S.I. & González-Delgado, A.D., 2019. "Computer-aided environmental and exergy analysis as decision-making tools for selecting bio-oil feedstocks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 42-57.
    2. Olga Orynycz & Antoni Świć, 2018. "The Effects of Material’s Transport on Various Steps of Production System on Energetic Efficiency of Biodiesel Production," Sustainability, MDPI, vol. 10(8), pages 1-12, August.
    3. Sinan Erdogan & Cenk Sayin, 2018. "Selection of the Most Suitable Alternative Fuel Depending on the Fuel Characteristics and Price by the Hybrid MCDM Method," Sustainability, MDPI, vol. 10(5), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    3. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    4. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    5. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    6. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    7. Yannan Zhou & Jixia Huang & Mingxiang Huang & Yicheng Lin, 2019. "The Driving Forces of Carbon Dioxide Equivalent Emissions Have Spatial Spillover Effects in Inner Mongolia," IJERPH, MDPI, vol. 16(10), pages 1-14, May.
    8. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    9. Hamed Yassaghi & Simi Hoque, 2021. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use," Energies, MDPI, vol. 14(2), pages 1-27, January.
    10. Carlos Omar Trejo-Pech & James A. Larson & Burton C. English & T. Edward Yu, 2019. "Cost and Profitability Analysis of a Prospective Pennycress to Sustainable Aviation Fuel Supply Chain in Southern USA," Energies, MDPI, vol. 12(16), pages 1-18, August.
    11. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    12. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    13. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
    14. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
    15. Szulczyk, Kenneth R. & Badeeb, Ramez Abubakr, 2022. "Nontraditional sources for biodiesel production in Malaysia: The economic evaluation of hemp, jatropha, and kenaf biodiesel," Renewable Energy, Elsevier, vol. 192(C), pages 759-768.
    16. Bedi, Jatin & Toshniwal, Durga, 2021. "Can electricity demand lead to air pollution? A spatio-temporal analysis of electricity demand with climatic conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    17. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption," Applied Energy, Elsevier, vol. 236(C), pages 574-589.
    18. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    19. Shah, Syed Hasnain & Raja, Iftikhar Ahmed & Rizwan, Muhammad & Rashid, Naim & Mahmood, Qaisar & Shah, Fayyaz Ali & Pervez, Arshid, 2018. "Potential of microalgal biodiesel production and its sustainability perspectives in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 76-92.
    20. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:127-:d:125898. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.